Anthropic

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News
Anthropic
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  • 01
    Inviting hard questions
    Who decides the rules for AI? Can AI give my children a better future? Does AI make the world a more dangerous place? Can AI help scientists cure diseases? People have a lot of hard questions about AI. It’s our job to address them. Many people are positively disposed to AI. They already use it every day, and they see its potential for making our work and our lives less laborious, for changing the way we learn, for helping speed up scientific and technological progress, for creating new sources o
  • 02
    Ben Bernanke appointed to Anthropic’s Long-Term Benefit Trust
    Anthropic's Long-Term Benefit Trust (LTBT) has appointed Dr. Ben Bernanke, a Distinguished Fellow at the Brookings Institution and former Chair of the Federal Reserve, as its newest member. He joins an independent body that works to hold Anthropic to its mission: the responsible development of advanced AI for the long-term benefit of humanity. Bernanke led the Federal Reserve from 2006 to 2014, steering the central bank through the 2008 global financial crisis and the recovery that followed. Bef
  • 03
    Introducing a way to reflect on how you use Claude
    Today we're introducing, in beta, a new way to reflect on and refine how you use Claude. In our interviews with users, a common theme that’s emerged is a desire to better understand how, exactly, can AI be integrated into daily life. How often should someone use AI? How can it be used most effectively? When is AI suited to a task, and when is it better left to a human? We built this feature to help answer these types of questions. It lets you easily track and visualize how you use Claude, and de
  • 04
    Government of Alberta uses Claude to find and fix cybersecurity vulnerabilities across government systems
    Since 2025, the Government of Alberta has been using Claude Code with both Opus and Sonnet models to review its systems, find vulnerabilities, and fix them. A team inside Alberta’s Ministry of Technology and Innovation scanned 466 million lines of code in 20 hours, remediated security gaps across its systems, and built new tools to make those systems safer. We’re sharing details of their experience as an example of how government agencies can use Claude and Claude Code to secure their systems at
  • 05
    More details on Fable 5’s cyber safeguards and our jailbreak framework
    Claude Fable 5 has been re-deployed and is now available globally for all users. We’re taking this opportunity to share further information in two areas. First, we provide more information on the cybersecurity safeguards —specifically, the safety classifiers —that we launched with the model. These are the AI systems that accompany the model that detect and block dangerous (or potentially dangerous) cybersecurity uses. Here, we provide a detailed list of the types of harms Fable 5’s classifiers a
  • 06
    Introducing Claude Sonnet 5
    Claude Sonnet 5 is built to be the most agentic Sonnet model yet. It can make plans, use tools like browsers and terminals, and run autonomously at a level that, just a few months ago, required larger and more expensive models. For many developers, the agentic AI era began with Sonnet-class models: Claude Sonnet 3.5, 3.6, and 3.7 were the first models that showed impressive skills in coding and tool use. More recently, though, the clearest gains in agentic capabilities have been in our Opus-clas
  • 07
    Redeploying Fable 5
    Update Claude Fable 5 and Mythos 5 redeployed Jul 1, 2026 Access to Claude Fable 5 and Mythos 5 is now restored. On Friday, June 12, the US government applied export controls to our newest models, Claude Fable 5 and Claude Mythos 5. This required us to restrict access to foreign nationals, whether inside or outside the United States. Because the order took effect immediately and we had no reliable way to verify nationality in real-time, we suspended access to both models for all users. As of tod
  • 08
    Claude Science, an AI workbench for scientists, is now available
    AI has the potential to dramatically accelerate the pace of scientific discovery and the development of healthcare interventions. Since launching our efforts in the life sciences last fall, we’ve worked to improve our model capabilities, make connections to the scientific ecosystem via MCPs and skills, and launch partnerships in an effort to realize this potential. Today, we’re introducing our most significant expansion of these efforts: Claude Science , an AI workbench for scientists. Claude Sc
  • 09
    Introducing Claude Tag
    Claude Tag is a new way for teams to work with Claude. We’re starting on Slack, which Claude can join as a team member. Grant Claude access to selected channels, and connect it to whichever tools, data—and even codebases—you choose. Then, anyone in the channel can tag @Claude in, and delegate tasks to it while they focus on other work. Claude builds context by remembering relevant information from the channels it’s in, and can plan out tasks to complete in the future. We see Claude Tag as the be
  • 10
    Anthropic opens Seoul office and announces new partnerships across the Korean AI ecosystem
    We’ve just opened our Seoul office . Alongside it, we’re announcing new partnerships across the Korean AI ecosystem, with the enterprises, startups, and researchers behind some of the most ambitious uses of Claude. We’ve also signed a Memorandum of Understanding (MOU) with Korea’s Ministry of Science and ICT to advance AI safety. “What I see in Korea are teams who understand that innovation and safety are two sides of the same coin,” said KiYoung Choi, Representative Director of Korea at Anthrop
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Research
Anthropic
4分钟前更新
  • 01
    An off switch for dual use knowledge in AI models
    This post describes research conducted by AE Studio in collaboration with Anthropic. A frontier AI model is, among other things, a large store of knowledge. Some of that knowledge is dual use , meaning it can be used for good or for bad. For example, knowledge of cybersecurity can help patch critical security vulnerabilities, or it can be used to exploit them. Knowledge of virology can help a researcher create a vaccine, but it can also help a malicious actor design a deadly pathogen. Ideally, w
  • 02
    A global workspace in language models
    As you read this sentence, circuits in your brain are adjusting your posture, controlling your breathing, and transforming lines and curves on the screen into recognizable words. Most of this processing is invisible to you. But some of what takes place in your brain you do have access to—an image that pops into your head, or a deliberate plan you make about where to go shopping. Neuroscientists and philosophers sometimes refer to the latter type of brain activity as “consciously accessible,” to
  • 03
    Anthropic Economic Index report: Cadences
    Introduction One year ago, most Claude usage took the form of a conversation between a user and an assistant. With the rapid growth of Claude Code and Cowork, Claude sessions now increasingly consist of long-running agentic tasks. Chat transcripts no longer fully capture how people are using AI, and our methods for studying Claude’s economic impacts have had to adapt. To keep pace, we made several changes to our data pipeline for the Economic Index. In this version, we: Sample data at a higher r
  • 04
    Project Fetch: Phase two
    Michael Ilie, C. Daniel Freeman, and Kevin K. Troy In August 2025, we ran an experiment to see how much Claude could help Anthropic employees—who were not robotics experts—perform sophisticated (and amusing) tasks with an off-the-shelf robotic quadruped (henceforth, a robodog). We called this Project Fetch. We found that access to our state-of-the-art model at the time (Claude Opus 4.1) helped one team substantially outperform the other, who had to rely only on the internet and their own ingenui
  • 05
    Agentic coding and persistent returns to expertise
    Key findings Building on prior work , we introduce a framework for studying interactive agentic coding based on a privacy-preserving analysis of ~400,000 Claude Code sessions from between October 2025 and April 2026. We evaluate the composition of tasks, human-AI collaboration, and success rates. In a typical session, people make most of the planning decisions (what to do) and Claude makes most of the execution decisions (how to do it). The greater domain expertise a person brings to a session,
  • 06
    Paving the way for agents in biology
    Written by Laura Luebbert. Based on research by Ferdous Nasri, Sarah Gurev, Patrick Varilly, Krithik Ramesh, Nuala A. O’Leary, Jonah Cool, Bernhard Y. Renard, Pardis Sabeti, and Laura Luebbert. In this post, Laura Luebbert argues that we need to make biological data infrastructure more agent-friendly. As a case study, she and her team tasked scientific research agents (Claude, Biomni Open Source (Biomni OSS) 1 , Edison Analysis, 2 GPT) to retrieve the sequence data from NCBI Virus, a database vi
  • 07
    Measuring LLMs’ impact on N-day exploits
    Winnie Xiao, Tim Abbott, Nicholas Carlini, Newton Cheng, David Forsythe, Keane Lucas, Milad Nasr, and Shikhar Sakhuja For the last few months, we’ve been writing about large language models’ cybersecurity capabilities. For the most part, we’ve focused on zero-days—vulnerabilities that are unknown to the software’s maintainers. But a large fraction of real-world harm comes from N-days : vulnerabilities that have already been publicly disclosed, but only patched on some devices. Attackers exploit
  • 08
    Making Claude a chemist
    We’re working with world-class synthetic, computational, and analytical chemists to make Claude better at chemistry. In this post, we share our first work as part of this effort, in which Anthropic chemist, David Kamber, examines how Claude performs on a chemist’s most common analytical input, an NMR spectrum. When working with molecules, chemists move between hand-drawn structures on a whiteboard, instrument readouts, database query strings, and the technical notations of patents and publicatio
  • 09
    Mapping AI-enabled cyber threats: Insights from the LLM ATT&CK Navigator
    Kyla Guru, Alex Moix, and Jacob Klein We’ve spent the past year investigating how threat actors are weaponizing AI to conduct cyber operations. Today, we’re sharing a new analysis that maps these real-world attacks onto the MITRE ATT&CK® framework , a database of tactics and techniques used by cyberattackers. Doing so reveals patterns that challenge traditional assumptions about cybersecurity—for example, the level of risk a threat actor poses can be assessed via metrics like technical sophistic
  • 10
    What we learned mapping a year’s worth of AI-enabled cyber threats
    As AI transforms the nature of and methods behind cyberattacks, how well do the techniques and frameworks used by the security community hold up? In a new report, we seek to answer that question. We examine 832 accounts that were banned for malicious cyber activity between March 2025 and March 2026 and map them onto MITRE ATT&CK , a longstanding database of the tactics and techniques used by cyberattackers. We published some of these results in Verizon’s 2026 Data Breach Investigations Report (D
  • 11
    Coding agents in the social sciences
    Summary We present results from a survey of 1,260 social scientists about AI and coding agent use, fielded in February and March 2026. The vast majority of respondents (81%) have tried using AI chatbots in research, particularly for writing code and editing prose. But only 20% have adopted coding agents—tools like Claude Code that autonomously write and execute analysis code—into their work. There are sharp disparities in use of coding agents. Twice as many researchers with typically male names
  • 12
    Project Glasswing: An initial update
    Last month, we launched Project Glasswing , our collaborative effort to secure the world’s most critical software before increasingly capable AI models can be turned against it. Since then, we and our approximately 50 partners have used Claude Mythos Preview to find more than ten thousand high- or critical-severity vulnerabilities across the most systemically important software in the world. Progress on software security used to be limited by how quickly we could find new vulnerabilities. Now it
  • 13
    Measuring LLMs’ ability to develop exploits
    Newton Cheng, Keane Lucas, Winnie Xiao, Nicholas Carlini, and Milad Nasr Introduction Claude Mythos Preview ’s ability to develop exploits is a step-change over previous frontier models. This was one of our primary motivations for rolling out the model carefully through Project Glasswing rather than through a general release. Mythos Preview is capable of finding complex vulnerabilities, but what concerned us most in our internal testing was that Mythos Preview could both turn vulnerabilities int
  • 14
    2028: Two scenarios for global AI leadership
    We’re releasing a new paper that explains our views on the competition on AI between the US and China. It’s essential that the US and its allies stay ahead of authoritarian governments like the Chinese Communist Party, or CCP. AI will soon become powerful enough to be used to repress citizens at unprecedented scale, and even to alter the balance of power among nations . And since AI is advancing more quickly by the day, we have only a limited period of time to set the conditions of the competiti
  • 15
    Teaching Claude why
    Last year, we released a case study on agentic misalignment . In experimental scenarios, we showed that AI models from many different developers sometimes took egregiously misaligned actions when they encountered (fictional) ethical dilemmas. For example, in one heavily discussed example, the models blackmailed engineers to avoid being shut down. When we first published this research, our most capable frontier models were from the Claude 4 family. This was also the first model family for which w
  • 16
    Natural Language Autoencoders: Turning Claude’s thoughts into text
    When you talk to an AI model like Claude, you talk to it in words. Internally, Claude processes those words as long lists of numbers, before again producing words as its output. These numbers in the middle are called activations— and like neural activity in the human brain, they encode Claude’s thoughts. Also like neural activity, activations are difficult to understand. We can’t easily decode them to read Claude’s thoughts. Over the past few years, we’ve developed a range of tools (like sparse
  • 17
    Donating our open-source alignment tool
    In October 2025, we launched Petri , an open-source toolbox of alignment tests that can be applied to any large language model. Petri, which was developed as part of our Anthropic Fellows program, can be used to rapidly and easily test AI models for concerning tendencies like deception, sycophancy, and cooperation with harmful requests. It’s part of our efforts to develop alignment tools that are open and useful for the whole AI development community. Petri has been part of our alignment assessm
  • 18
    Focus areas for The Anthropic Institute
    At The Anthropic Institute (TAI), we’ll be using the information we can access from within a frontier lab to investigate AI’s impact on the world, and sharing our learnings with the public. Here, we’re sharing the questions that drive our research agenda. Our agenda focuses on four areas for research: Economic diffusion Threats and resilience AI systems in the wild AI-driven R&D In Core Views on AI Safety , we wrote that doing effective safety research required close contact with frontier AI sys
  • 19
    How people ask Claude for personal guidance
    People don’t just come to Claude for code reviews or meeting summaries. They ask whether to take the job, how to talk to their crush, if they should move halfway across the world. Using our privacy-preserving analysis tool on a random sample of 1 million claude.ai conversations, we found that roughly 6% were people coming to Claude for personal guidance—seeking not just information but perspective on what to do next. In this study, we looked at what types of guidance people ask of Claude. We exp
  • 20
    Evaluating Claude’s bioinformatics research capabilities with BioMysteryBench
    In this post, Brianna , a researcher on the discovery team, shares results from a recent bioinformatics benchmarking effort. Almost as soon as large language models could hold a conversation, people started asking how they’d stack up against human experts. Could models pass the bar exam? Could they answer medical licensing questions, or solve Olympiad math problems? Such benchmarks —self-contained sets of human-vetted problems designed to evaluate a capability of a model—have now become a source
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Engineering
Anthropic
5分钟前更新
  • 01
    An update on recent Claude Code quality reports
    Over the past month, we’ve been looking into reports that Claude’s responses have worsened for some users. We’ve traced these reports to three separate changes that affected Claude Code, the Claude Agent SDK, and Claude Cowork. The API was not impacted. All three issues have now been resolved as of April 20 (v2.1.116). In this post, we explain what we found, what we fixed, and what we’ll do differently to ensure similar issues are much less likely to happen again. We take reports about degradati
  • 02
    Scaling Managed Agents: Decoupling the brain from the hands
    Get started with Claude Managed Agents by following our docs . A running topic on the Engineering Blog is how to build effective agents and design harnesses for long-running work . A common thread across this work is that harnesses encode assumptions about what Claude can’t do on its own. However, those assumptions need to be frequently questioned because they can go stale as models improve. As just one example, in prior work we found that Claude Sonnet 4.5 would wrap up tasks prematurely as it
  • 03
    How we built Claude Code auto mode: a safer way to skip permissions
    By default, Claude Code asks users for approval before running commands or modifying files. This keeps users safe, but it also means a lot of clicking "approve." Over time that leads to approval fatigue, where people stop paying close attention to what they're approving. Users have two solutions for avoiding this fatigue: a built-in sandbox where tools are isolated to prevent dangerous actions, or the --dangerously-skip-permissions flag that disables all permission prompts and lets Claude act fr
  • 04
    Harness design for long-running application development
    Written by Prithvi Rajasekaran, a member of our Labs team. Over the past several months I’ve been working on two interconnected problems: getting Claude to produce high-quality frontend designs, and getting it to build complete applications without human intervention. This work originated with earlier efforts on our frontend design skill and long-running coding agent harness , where my colleagues and I were able to improve Claude’s performance well above baseline through prompt engineering and h
  • 05
    Eval awareness in Claude Opus 4.6’s BrowseComp performance
    BrowseComp is an evaluation designed to test how well models can find hard-to-locate information on the web. Like many benchmarks, it is vulnerable to contamination: answers leak onto the public web through academic papers, blog posts, and GitHub issues, and a model running the eval can encounter them in search results. When we evaluated Claude Opus 4.6 on BrowseComp in a multi-agent configuration, we found nine examples of this kind of contamination across 1,266 BrowseComp problems. However, we
  • 06
    Quantifying infrastructure noise in agentic coding evals
    Agentic coding benchmarks like SWE-bench and Terminal-Bench are commonly used to compare the software engineering capabilities of frontier models—with top spots on leaderboards often separated by just a few percentage points. These scores are often treated as precise measurements of relative model capability and increasingly inform decisions about which models to deploy. However, we’ve found that infrastructure configuration alone can produce differences that exceed those margins. In internal ex
  • 07
    Building a C compiler with a team of parallel Claudes
    Written by Nicholas Carlini, a researcher on our Safeguards team. I've been experimenting with a new approach to supervising language models that we’re calling "agent teams." With agent teams, multiple Claude instances work in parallel on a shared codebase without active human intervention. This approach dramatically expands the scope of what's achievable with LLM agents. To stress test it, I tasked 16 agents with writing a Rust-based C compiler, from scratch, capable of compiling the Linux kern
  • 08
    Designing AI-resistant technical evaluations
    Written by Tristan Hume, a lead on Anthropic's performance optimization team. Tristan designed—and redesigned—the take-home test that's helped Anthropic hire dozens of performance engineers. Evaluating technical candidates becomes harder as AI capabilities improve. A take-home that distinguishes well between human skill levels today may be trivially solved by models tomorrow—rendering it useless for evaluation. Since early 2024, our performance engineering team has used a take-home test where ca
  • 09
    Demystifying evals for AI agents
    Introduction Good evaluations help teams ship AI agents more confidently. Without them, it’s easy to get stuck in reactive loops—catching issues only in production, where fixing one failure creates others. Evals make problems and behavioral changes visible before they affect users, and their value compounds over the lifecycle of an agent. As we described in Building effective agents , agents operate over many turns: calling tools, modifying state, and adapting based on intermediate results. Thes
  • 10
    Effective harnesses for long-running agents
    As AI agents become more capable, developers are increasingly asking them to take on complex tasks requiring work that spans hours, or even days. However, getting agents to make consistent progress across multiple context windows remains an open problem. The core challenge of long-running agents is that they must work in discrete sessions, and each new session begins with no memory of what came before. Imagine a software project staffed by engineers working in shifts, where each new engineer arr
  • 11
    Introducing advanced tool use on the Claude Developer Platform
    The future of AI agents is one where models work seamlessly across hundreds or thousands of tools. An IDE assistant that integrates git operations, file manipulation, package managers, testing frameworks, and deployment pipelines. An operations coordinator that connects Slack, GitHub, Google Drive, Jira, company databases, and dozens of MCP servers simultaneously. To build effective agents , they need to work with unlimited tool libraries without stuffing every definition into context upfront. O
  • 12
    Code execution with MCP: Building more efficient agents
    The Model Context Protocol (MCP) is an open standard for connecting AI agents to external systems. Connecting agents to tools and data traditionally requires a custom integration for each pairing, creating fragmentation and duplicated effort that makes it difficult to scale truly connected systems. MCP provides a universal protocol—developers implement MCP once in their agent and it unlocks an entire ecosystem of integrations. Since launching MCP in November 2024, adoption has been rapid: the co
  • 13
    Beyond permission prompts: making Claude Code more secure and autonomous
    In Claude Code , Claude writes, tests, and debugs code alongside you, navigating your codebase, editing multiple files, and running commands to verify its work. Giving Claude this much access to your codebase and files can introduce risks, especially in the case of prompt injection. To help address this, we’ve introduced two new features in Claude Code built on top of sandboxing, both of which are designed to provide a more secure place for developers to work, while also allowing Claude to run m
  • 14
    Equipping agents for the real world with Agent Skills
    Update: We've published Agent Skills as an open standard for cross-platform portability. (December 18, 2025) As model capabilities improve, we can now build general-purpose agents that interact with full-fledged computing environments. Claude Code , for example, can accomplish complex tasks across domains using local code execution and filesystems. But as these agents become more powerful, we need more composable, scalable, and portable ways to equip them with domain-specific expertise. This led
  • 15
    Effective context engineering for AI agents
    After a few years of prompt engineering being the focus of attention in applied AI, a new term has come to prominence: context engineering . Building with language models is becoming less about finding the right words and phrases for your prompts, and more about answering the broader question of “what configuration of context is most likely to generate our model’s desired behavior?" Context refers to the set of tokens included when sampling from a large-language model (LLM). The engineering prob
  • 16
    A postmortem of three recent issues
    Between August and early September, three infrastructure bugs intermittently degraded Claude's response quality. We've now resolved these issues and want to explain what happened. In early August, a number of users began reporting degraded responses from Claude. These initial reports were difficult to distinguish from normal variation in user feedback. By late August, the increasing frequency and persistence of these reports prompted us to open an investigation that led us to uncover three separ
  • 17
    Writing effective tools for agents — with agents
    The Model Context Protocol (MCP) can empower LLM agents with potentially hundreds of tools to solve real-world tasks. But how do we make those tools maximally effective? In this post, we describe our most effective techniques for improving performance in a variety of agentic AI systems 1 . We begin by covering how you can: Build and test prototypes of your tools Create and run comprehensive evaluations of your tools with agents Collaborate with agents like Claude Code to automatically increase t
  • 18
    Desktop Extensions: One-click MCP server installation for Claude Desktop
    File extension update Sep 11, 2025 Claude Desktop Extensions now use the .mcpb (MCP Bundle) file extension instead of .dxt. Existing .dxt extensions will continue to work, but we recommend developers use .mcpb for new extensions going forward. All functionality remains the same - this is purely a naming convention update. — When we released the Model Context Protocol (MCP) last year, we saw developers build amazing local servers that gave Claude access to everything from file systems to database
  • 19
    How we built our multi-agent research system
    Claude now has Research capabilities that allow it to search across the web, Google Workspace, and any integrations to accomplish complex tasks. The journey of this multi-agent system from prototype to production taught us critical lessons about system architecture, tool design, and prompt engineering. A multi-agent system consists of multiple agents (LLMs autonomously using tools in a loop) working together. Our Research feature involves an agent that plans a research process based on user quer
  • 20
    How we contain Claude across products
    Twelve months ago, we'd have rejected out of hand the idea of granting Claude access sufficient to take down an internal Anthropic service. Today that level of access is routine, and Anthropic developers are more productive for it. The risk of these deployments has two components: how likely a failure is, and how much damage one could do. Progress on safeguards and model training has steadily driven down the first; the second—the theoretical blast radius—only grows as capabilities and access exp
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